Bandit-based Monte-Carlo planning for the single-machine total weighted tardiness scheduling problem

Authors:
Gabriel Kronberger;Roland Braune
Affiliations:
Upper Austria University of Applied Sciences, Research Center Hagenberg, Hagenberg, Austria;Upper Austria University of Applied Sciences, Research Center Hagenberg, Hagenberg, Austria
Venue:
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
Year:
2007

Citing 3
Cited 0

Finite-time Analysis of the Multiarmed Bandit Problem

Machine Learning
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem

INFORMS Journal on Computing
Bandit based monte-carlo planning

ECML'06 Proceedings of the 17th European conference on Machine Learning

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Abstract

The balance of exploration and exploitation is the essence of any successful meta-heuristic. The Multi-armed Bandit Problem represents a simple form of this general dilemma. This paper describes two heuristic optimization methods that use a simple yet efficient allocation strategy for the bandit problem called UCB1 to control the optimization process. The algorithms are applied to the well known Single Machine Total Weighted Tardiness Problem and the results compared to the results of other successful meta-heuristics for this scheduling problem.